Supervised contrastive replay (SCR) [1]} adapts SCL for the problem of class-incremental online continual learning and provides the state-of-the-art performance, where the main difference between SCR and SCL comes from the transformation module, as visualised in Figure REF  and Figure REF  respectively: For SCL, its transformation module \(\mathcal {H}\)  applies two distinct transformations on the input image to construct the positive pair, while a positive pair in SCR is built upon an input image \(x_i\)  and its random view \(\tilde{x}_i\) , thus SCR in general has lower randomness than SCL. With denoting the original images as \(\mathbf {V}_{\text{ori}}=\lbrace (\tilde{x}_{i}, y_{i}) \mid \tilde{x}_{i}=x_i\rbrace \) , the training views for SCR are then formed by:
\(\mathbf {V}_{\text{SCR}} = \bigcup \limits _{i=1}^\mathbb {B} ( \lbrace (\tilde{x}_{i},y_{i}) \mid \tilde{x}_{i}=H(x_i)\rbrace \cup \mathbf {V}_{\text{ori}})\) 
